# Installing libraries
library(TAM) # for Rasch modeling
library(WrightMap) # to build item-person (Wright) maps
library(lsr) # to calculate Cohen's d
library(DescTools) # to calculate eta-squared
library(Rmisc) # to calculate summary statistics
library(tidyverse) # for plotting
library(cowplot) # for image building
library(magick) # for image processing
library(knitr) # for knitting
# Create logo banner for plots
earthlab_orig <- image_read(path = "earth-lab-logo-white.png") %>%
image_scale("x80")
twitter_orig <- image_read(path = "plot-footer-twitter.png") %>%
image_scale("x70")
black_banner <- image_read(path = "black-banner.png")
earthlab_logo <- image_composite(image_scale(black_banner, "1000x100"), earthlab_orig, offset = "+30+10")
twitter_logo <- image_composite(image_scale(black_banner, "1000x100"), twitter_orig, offset = "+540+15")
logo <- image_append(image_scale(c(earthlab_logo, twitter_logo)), stack = FALSE)
logo
A total of 53 consenting participants provided demographic information related to gender, race & ethnicity through a series of items included on the pre-program survey instrument, administered prior to the start of the technical workshops.
##
## Call:
## lm(formula = Ability ~ Trial + Dimension + Cohort + Cohort *
## Dimension, data = abil_trial_dimension_all6)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.228 -1.128 0.032 0.982 5.022
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 2.75115 0.32182 8.549
## TrialBefore -2.93001 0.16283 -17.994
## DimensionData Science Practices -0.05436 0.44032 -0.123
## DimensionData Science Skills 0.16079 0.44032 0.365
## DimensionPython Skills 2.11823 0.44032 4.811
## DimensionScience Identity -0.12235 0.44032 -0.278
## CohortYear 2 -0.09295 0.46503 -0.200
## CohortYear 3 -0.13972 0.42259 -0.331
## DimensionData Science Practices:CohortYear 2 -0.24793 0.65766 -0.377
## DimensionData Science Skills:CohortYear 2 0.32802 0.66517 0.493
## DimensionPython Skills:CohortYear 2 0.72306 0.66517 1.087
## DimensionScience Identity:CohortYear 2 -0.59235 0.66517 -0.891
## DimensionData Science Practices:CohortYear 3 -0.12938 0.59763 -0.216
## DimensionData Science Skills:CohortYear 3 0.12335 0.60141 0.205
## DimensionPython Skills:CohortYear 3 0.70361 0.60141 1.170
## DimensionScience Identity:CohortYear 3 -0.62037 0.60141 -1.032
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## TrialBefore < 2e-16 ***
## DimensionData Science Practices 0.902
## DimensionData Science Skills 0.715
## DimensionPython Skills 2.05e-06 ***
## DimensionScience Identity 0.781
## CohortYear 2 0.842
## CohortYear 3 0.741
## DimensionData Science Practices:CohortYear 2 0.706
## DimensionData Science Skills:CohortYear 2 0.622
## DimensionPython Skills:CohortYear 2 0.278
## DimensionScience Identity:CohortYear 2 0.374
## DimensionData Science Practices:CohortYear 3 0.829
## DimensionData Science Skills:CohortYear 3 0.838
## DimensionPython Skills:CohortYear 3 0.243
## DimensionScience Identity:CohortYear 3 0.303
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.761 on 452 degrees of freedom
## Multiple R-squared: 0.5314, Adjusted R-squared: 0.5158
## F-statistic: 34.17 on 15 and 452 DF, p-value: < 2.2e-16
## Analysis of Variance Table
##
## Response: Ability
## Df Sum Sq Mean Sq F value Pr(>F)
## Trial 1 1004.44 1004.44 323.7867 <2e-16 ***
## Dimension 4 563.71 140.93 45.4291 <2e-16 ***
## Cohort 2 1.34 0.67 0.2158 0.8060
## Dimension:Cohort 8 20.59 2.57 0.8295 0.5769
## Residuals 452 1402.18 3.10
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## eta.sq eta.sq.part
## Trial 0.3356796615 0.4173656492
## Dimension 0.1883110526 0.2866604141
## Cohort 0.0004473933 0.0009538292
## Dimension:Cohort 0.0068797627 0.0144690270
##
## Welch Two Sample t-test
##
## data: comfortpost$Ability and comfortpre$Ability
## t = 10.27, df = 87.882, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.950954 2.887157
## sample estimates:
## mean of x mean of y
## 2.415192354 -0.003863267
## [1] 2.096379
##
## Welch Two Sample t-test
##
## data: confpost$Ability and confpre$Ability
## t = 7.5006, df = 92.239, p-value = 3.837e-11
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.531676 2.634901
## sample estimates:
## mean of x mean of y
## 2.074584526 -0.008704105
## [1] 1.531062
##
## Welch Two Sample t-test
##
## data: idpost$Ability and idpre$Ability
## t = 3.3174, df = 82.505, p-value = 0.001353
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.6008267 2.4004040
## sample estimates:
## mean of x mean of y
## 1.43789668 -0.06271865
## [1] 0.6917187
##
## Welch Two Sample t-test
##
## data: techconfpost$Ability and techconfpre$Ability
## t = 10.45, df = 89.127, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 2.431845 3.573682
## sample estimates:
## mean of x mean of y
## 3.0032369769 0.0004732148
## [1] 2.179052
##
## Welch Two Sample t-test
##
## data: techcomfortpost$Ability and techcomfortpre$Ability
## t = 15.426, df = 89.051, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 4.968754 6.437964
## sample estimates:
## mean of x mean of y
## 6.641079 0.937720
## [1] 3.216642